BD jupyter-notebook
Use when the user asks to create, scaffold, or edit Jupyter notebooks (`.ipynb`) for experiments, explorations, or tutorials; prefer the bundled templates and run the helper script `new_notebook.py` to generate a clean starting notebook.
Generates and edits Jupyter notebooks for experiments and tutorials via AI agent. Promises to use bundled templates and a helper script `new_notebook.py` for clean notebook scaffolding.
Eight files and one script in the folder. Quality score 83/100, process score 45/100. No critical issues found, but broken file references detected. Not tested on models. Supports all major agent platforms from Claude to DeepSeek.
Install if you need basic notebook generation for exploratory work. Broken references are a drawback but not a blocker—the script and templates are functional. Just verify files load correctly in your environment.
ipynb) for experiments, explorations, or tutorials; prefer the bundled templates and run the helper script newnotebook.py to generate a clean starting notebook.
As a process D 45/100 · Unfinished process — References files that are not bundled: assets/experiment-template.ipynb, assets/tutorial-template.ipynb
How to improve
- The text references files that are not there: add them or drop the references.
- Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
- A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.
Guard findings · 0
✓ No critical or high findings
Files scanned: 8. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
missing-refreference to a missing file: assets/experiment-template.ipynb - warning
missing-refreference to a missing file: assets/tutorial-template.ipynb
Process rating: all ten parameters 45/100
- 0Tools and files. 2 referenced file(s) missing: assets/experiment-template.ipynb, assets/tutorial-template.ipynb
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 3 mutating operations with no state check
- 60Failures and branches. 2 branches
- 70When it triggers. States when to use, but not when not to
- 100Steps. 25 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 967 tokens
Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.
Quality signals
- +5Description has no quoted example phrases that should trigger the skill
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- +2Single-language instructions
- +3Description length 237: enough signal without eating the budget
- +4Structure: 10 headings
- +3Step-by-step instructions: 25 items
- +4Has examples (4 code blocks)
- +4Reference files are cited in the instructions (4 of 4)
- +3All 1 scripts are documented
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 83.